It is common practice to install surveillance cameras in locations such as railroad stations and particular facilities and to analyze images captured with the surveillance cameras to perform various kinds of determination. As one example, a person or an object that stays in a surveillance area for an unusually long period is identified as a suspicious person or a suspicious object.
A known related technique is a behavior analysis method that tracks a specific person and analyzes behavior of the person. In the behavior analysis, for example, one camera or multiple cameras whose coverage areas overlap one another are used to recognize the location of a specific person and changes in the location of the person with time are tracked, thereby identifying where and how long the person stayed.
A person recognition method is also known that performs face matching in order to recognize a specific person in a captured image. PTL 1 describes a facial image recognition apparatus that is intended to speed up facial image recognition processing and to simplify registration work. In the facial image recognition apparatus described in PTL 1, a full-face facial image and a non-full-faced average facial image of a person to be recognized are registered in advance and features of a facial area extracted from an image is compared with the registered facial images to recognize a facial image in the image.
PTL 2 describes a suspicious person detecting apparatus that automatically detects a suspicious person from a camera image. The apparatus described in PTL 2 periodically captures images with cameras capable of taking images of surroundings of a vehicle in all directions, calculates quantities of features of behavior of an extracted person at predetermined intervals, and determines, from the frequency distribution of the quantities of features of behavior, whether the person is a suspicious person.
PTL 3 describes an image processing apparatus that associates objects in an image with one another. In the apparatus described in PTL 3, an evaluation means evaluates an object detected by an object detection means. Then a relation evaluation means evaluates other objects associated with the object evaluated by the evaluation means based on the evaluation. In this way, a second object associated with a first object is evaluated based on the evaluation of the first object.